There are 1 repository under factor-model topic.
A curated list of awesome algorithmic trading frameworks, libraries, software and resources
Build a statistical risk model using PCA. Optimize the portfolio using the risk model and factors using multiple optimization formulations.
Covariance Matrix Estimation via Factor Models
Attribution and optimisation using a multi-factor equity risk model.
This repository shows the application of PCA technique for risk factor modelling of financial securities.
Package to build risk model for factor pricing model
Unsupervised learning coupled with applied factor analysis to the five-factor model (FFM), a taxonomy for personality traits used to describe the human personality and psyche, via descriptors of common language and not on neuropsychological experiments. Used kmeans clustering and feature scaling (min-max normalization).
Repository for the AugmentedPCA Python package.
Regularized estimation of high-dimensional FAVAR models
Data Science Project: Replication of "Forest Through the Trees: Building Cross-Sections of Stock Returns" - creation of assets to test validity of factor models with Python
Replication Study on Idiosyncratic Momentum Strategy
This thesis investigates the relationship between employee sentiment, proxied by Glassdoor reviews and ratings, and excessive returns on corresponding bonds. While sentiment analysis is well studied for probing into how companies are perceived by investors or the general public, it is a novel idea to exploit sentiment of employees, which enables us to capture very important information for assessing companies' governance. Although a few studies scrutinising the relation employee sentiment and future stock returns have already appeared, this is the first attempt, to the best of my knowledge, to place this analysis to the universe of corporate bonds.
Factor Modeling
Package to build universes for factor pricing model
This is a tentative pytorch implementation of the paper "Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounders"
data segmentation and forecasting for VAR-driven factor models